High-Dimensional Statistics, Network Modeling, Statistical Machine Learning, Deep Neural Networks, Tensor Factor Models
I am currently a Robert and Sara Lumpkins Postdoctoral Research Associate at the University of Notre Dame (ND) in the U.S., within the Department of Applied and Computational Mathematics and Statistics. I received my Ph.D. in Statistics from the Gregory and Paula Chow Institute for Studies in Economics, Xiamen University (XMU) in 2025, where I was advised by Prof. Wei Zhong. During 2023-2024, I was a visiting researcher at the Australian National University (ANU), College of Business and Economics. I completed my academic master’s courses in Mathematical Statistics at Xiamen University, the Wang Yanan Institute for Studies in Economics (WISE) and my Bachelor of Science in Statistics at Xiamen University, the School of Economics.
My research focuses on high-dimensional statistics, deep neural networks, spatial models, tensor factor models, and statistical network modeling, particularly developing novel methodologies that bridge statistical theory with practical problems. I am also interested in interdisciplinary research at the intersection of statistics with machine learning, econometrics, and music.
Working Papers
Xing Z., Yu X. (2026+). “NetworkNet: A Deep Neural Network Approach for Random Networks with Sparse Nodal Attributes and Complex Nodal Heterogeneity” arXiv. Download
Xing Z., Zhong W. (2026+). “PALMS: Parallel Adaptive Lasso with Multi-directional Signals for Latent Network Reconstruction.” arXiv. Download
Xing Z., Wang Y. X. R., Wood A.T.T., Zou T. (2026+). “Regularization and Selection in a Directed Network Model with Nodal Homophily and Nodal Effects.” arXiv. Download
Xing Z., Che C., Chen Y., Zhong W. (2026+). “FLAT: Fused Lasso Regression with Adaptive Minimum Spanning Tree with Applications on Thermohaline Circulation.” arXiv. Download
Publications
2025
- Xing, Z., Tan, H., Zhong, W., & Shi, L. (2025). CALMS: Constrained Adaptive Lasso with Multi-directional Signals for latent network reconstruction. Neurocomputing, 630, 129545. Download
2024
- Xing, Z., Wan, Y., Wen, J., & Zhong, W. (2024). GOLFS: Feature selection via combining both global and local information for high dimensional clustering. Computational Statistics, 39(5), 2651-2675. Download
See my Google Scholar for the latest publications.
Teaching Experience
- 08/2026 - 12/2026: Probability and Statistics for AI (ND)
- 01/2026 - 05/2026: Statistics in Life Sciences (ND)
- 09/2025 - 12/2025: High-dimensional Statistics (ND)
- 09/2024 - 12/2024: Regression Modelling (ANU)
- 09/2024 - 12/2024: Regression Modelling for Actuarial Studies (ANU)
- 03/2024 - 07/2024: Statistical Learning (ANU)
Talks
- Notre Dame, U.S.A., 2026 - AI Foundations Symposium
- Canberra, Australia, 2024 - CBE Statistics Seminar
- Canberra, Australia, 2023 - Summer Research Camp
- Kunming, China, 2023 - The Chinese Association for Applied Statistics
- Beijing, China, 2023 - The Joint Conference on Statistics and Data Science in China